| 2021 |
Tsygankov, Denis |
R21Activity Code Description:
To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Patterns of Aging and the Role of Biomarkers of Senescence @ Georgia Institute of Technology
ABSTRACT Over the past century, life expectancy has increased by 30 years. With that gain has come a dramatic rise in age-related disease and an urgent need to understand, prevent, and treat these conditions. While age-related diseases have diverse phenotypes, there is increasing recognition of common biological underpinnings with cellular senescence as the nexus linking subcellular changes due to epigenetic changes, DNA damage, and mitochondria dysfunction with a decline in health due to multi-morbidity. To begin unveiling the mechanism underlying senescence dynamics over the span of human life, this project brings together an interdisciplinary team of experts in mathematical modeling, the biology of aging, and clinical research. Specifically, the first aim of this integrative project is to determine if the p16INK4a mRNA ? a biomarker of aging and cellular senescence ? measured in T-cells reflects total senescence load at the systems level. The next goal is to determine how senescence dynamics is affected by age-associated diseases. Finally, the team will test the ability of their computational models of senescence at the cellular and organismal levels to predict donors? multi-morbidity status, establishing the link between senescence and clinical and functional decline. To achieve these objectives, the researchers will measure p16 mRNA levels in donors over the adult lifespan using an optimized, analytically validated, clinical-grade p16 assay. In parallel, they will develop a computational stochastic model of p16 levels in T-cells taking into account age-dependent modulation of the regeneration and attrition rates. The results of this p16-T-cell modeling will be compared with the computational whole-body-p16 model and the data from a p16-luciferase mouse model. The models of p16 expression will also be apply to compare the expression profiles and the underlying parameters for the healthy donor data and the data from cancer and heart disease patients. Then, the team will perform a correlation analysis of senescence and donors? health to determine whether p16 expression predicts dysfunction more readily in some organ systems vs. others, potentially reflecting differential susceptibility to senescence-associated disease. This project will make a significant contribution to understanding the phenomenon of senescence, its regulation and dynamics, and its role in physiological or pathological processes during human aging. These findings will serve as pilot data for an R01 application to expand p16 analysis to other cohorts and begin to establish comparisons between p16 and other potentially clinically relevant aging biomarkers such as DNA methylation panels and plasma proteomics panels.
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